Moving object detection and static map reconstruction in the environment with complex dynamic background are prone to incomplete moving object detection. In order to solve the problem， a new moving object detection method with hybrid vision system assisted by point cloud segmentation was proposed. Firstly， the PassThrough+RANdom SAmple Consensus （RANSAC） method was proposed to overcome large-area wall interference， so as to realize the point cloud ground point recognition. Secondly， the non-ground point data were projected to the image as feature points， and their optical flow motion vectors and artificial motion vectors were estimated to detect the dynamic points. Then， the dynamic threshold strategy was used to perform Euclidean clustering to the point cloud. Finally， the results of dynamic point detection and point cloud segmentation were integrated to completely extract the moving objects. In addition， the Octomap tool was used to convert the point cloud map into a 3D grid map in order to complete the map construction. Through the experimental results and data analysis， it can be seen that the proposed method can effectively improve the integrity of moving object detection， and reconstruct a low-loss， highly-practical static grid map.